import { APIResource } from "../core/resource.js";
import { APIPromise } from "../core/api-promise.js";
import { RequestOptions } from "../internal/request-options.js";
export declare class Embeddings extends APIResource {
    /**
     * Creates an embedding vector representing the input text.
     *
     * @example
     * ```ts
     * const createEmbeddingResponse =
     *   await client.embeddings.create({
     *     input: 'The quick brown fox jumped over the lazy dog',
     *     model: 'text-embedding-3-small',
     *   });
     * ```
     */
    create(body: EmbeddingCreateParams, options?: RequestOptions): APIPromise<CreateEmbeddingResponse>;
}
export interface CreateEmbeddingResponse {
    /**
     * The list of embeddings generated by the model.
     */
    data: Array<Embedding>;
    /**
     * The name of the model used to generate the embedding.
     */
    model: string;
    /**
     * The object type, which is always "list".
     */
    object: 'list';
    /**
     * The usage information for the request.
     */
    usage: CreateEmbeddingResponse.Usage;
}
export declare namespace CreateEmbeddingResponse {
    /**
     * The usage information for the request.
     */
    interface Usage {
        /**
         * The number of tokens used by the prompt.
         */
        prompt_tokens: number;
        /**
         * The total number of tokens used by the request.
         */
        total_tokens: number;
    }
}
/**
 * Represents an embedding vector returned by embedding endpoint.
 */
export interface Embedding {
    /**
     * The embedding vector, which is a list of floats. The length of vector depends on
     * the model as listed in the
     * [embedding guide](https://platform.openai.com/docs/guides/embeddings).
     */
    embedding: Array<number>;
    /**
     * The index of the embedding in the list of embeddings.
     */
    index: number;
    /**
     * The object type, which is always "embedding".
     */
    object: 'embedding';
}
export type EmbeddingModel = 'text-embedding-ada-002' | 'text-embedding-3-small' | 'text-embedding-3-large';
export interface EmbeddingCreateParams {
    /**
     * Input text to embed, encoded as a string or array of tokens. To embed multiple
     * inputs in a single request, pass an array of strings or array of token arrays.
     * The input must not exceed the max input tokens for the model (8192 tokens for
     * all embedding models), cannot be an empty string, and any array must be 2048
     * dimensions or less.
     * [Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken)
     * for counting tokens. In addition to the per-input token limit, all embedding
     * models enforce a maximum of 300,000 tokens summed across all inputs in a single
     * request.
     */
    input: string | Array<string> | Array<number> | Array<Array<number>>;
    /**
     * ID of the model to use. You can use the
     * [List models](https://platform.openai.com/docs/api-reference/models/list) API to
     * see all of your available models, or see our
     * [Model overview](https://platform.openai.com/docs/models) for descriptions of
     * them.
     */
    model: (string & {}) | EmbeddingModel;
    /**
     * The number of dimensions the resulting output embeddings should have. Only
     * supported in `text-embedding-3` and later models.
     */
    dimensions?: number;
    /**
     * The format to return the embeddings in. Can be either `float` or
     * [`base64`](https://pypi.org/project/pybase64/).
     */
    encoding_format?: 'float' | 'base64';
    /**
     * A unique identifier representing your end-user, which can help OpenAI to monitor
     * and detect abuse.
     * [Learn more](https://platform.openai.com/docs/guides/safety-best-practices#end-user-ids).
     */
    user?: string;
}
export declare namespace Embeddings {
    export { type CreateEmbeddingResponse as CreateEmbeddingResponse, type Embedding as Embedding, type EmbeddingModel as EmbeddingModel, type EmbeddingCreateParams as EmbeddingCreateParams, };
}
//# sourceMappingURL=embeddings.d.ts.map